observability-designer

To establish golden-signal observability, define latency, traffic, errors, and saturation SLI/SLO candidates before adding domain-specific signals. This ensures baseline system health is measured, and AI telemetry like prompt/token cost and guardrail outcomes are layered on top.

Correlation-first observability turns fragmented telemetry into actionable insight by defining an ID taxonomy for traces, requests, workflows, steps, tenants, and agents. It enforces propagation rules so every distributed signal can be linked end-to-end for root cause diagnosis.

Yes, you can monitor agentic and async systems by specifying workflow state transition signals alongside AI telemetry. This captures tool and retrieval signals, guardrail outcomes, and prompt/token cost data, ensuring full visibility into complex AI-native operations.

Managing telemetry costs and privacy requires adding sampling, retention, and redaction rules with strict privacy classification. This privacy-by-design approach ensures observability remains safe and sustainable in production while still detecting incidents reliably.

A correlation-first telemetry strategy requires consistent propagation of correlation ID hierarchies across all services; if any component fails to emit structured schemas or follow sampling rules, end-to-end traceability breaks, limiting root cause analysis and release-readiness validation.